Executive Director, AI/ML — Forecasting, Constraint Optimization, and Recommendation Systems

As an AI/ML Executive Director in our AI for Operations organization, you will lead the creation and operation of a complex, constraint-based optimization and recommendation ecosystem supporting the bank’s operations. You will integrate time-series predictions and demand forecasts with optimization models that pursue global, system-wide business objectives while satisfying interdependent operational constraints. This role combines significant business stakeholder management and communication across executive, product, business, and analytics partners with hands-on technical leadership. You will own the solution holistically—from business problem formulation and architecture design through data system creation, AI model development, optimization, deployment, and ongoing production operations.

Job Responsibilities

• Lead the design and delivery of a complex constraint-optimization recommendation system that translates time-series predictions and demand forecasts into actionable operational decisioning

• Formulate global, system-wide optimization objectives, decision variables, and hard and soft constraints with business partners; address competing goals, resource and capacity limits, cross-functional dependencies, and forecast uncertainty to deliver feasible recommendations.

• Develop and validate time-series and demand-driven predictive models, accounting for seasonality, changing demand patterns, and uncertainty; integrate forecast outputs into optimization workflows and evaluate both predictive accuracy and downstream decision quality.

• Build and lead a multidisciplinary team of applied AI/ML researchers, optimization specialists, and engineers; provide technical and career guidance while maintaining hands-on accountability for solution quality and execution.

• Drive significant business stakeholder management across executive leadership and product, business, analytics, data, and engineering partners; translate business needs into technical requirements, align priorities and success measures, and clearly communicate recommendations, trade-offs, risks, and progress to technical and nontechnical audiences. Collaborate with cross-functional partners including product, data and engineering.

• Own production operation of the prediction and optimization ecosystem, including model and data monitoring, forecast drift detection, constraint-feasibility checks, retraining, incident resolution, and continuous improvement of reliability and business outcomes.to

• Drive the full product and technical lifecycle, from problem definition, roadmap, and measurable deliverables through experimentation, back testing, scenario analysis, deployment, adoption, and ongoing performance evaluation.

• Serve as the hands-on technical architect and code contributor for the end-to-end ecosystem; design and build scalable data systems, ingestion and transformation pipelines, forecasting services, optimization engines, recommendation interfaces, and production orchestration with appropriate data quality, governance, and observability.

• Contribute to the continuous learning mindset of the organization by bringing in new knowledge, ideas, and perspectives.